Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle energy consumption

Abstract The ability to accurately predict the energy consumption of electric vehicles (EVs) is important for alleviating the range anxiety of drivers and is a critical foundation for the spatial planning, operation and management of charging infrastructures. Based on the GPS observations of 68 EVs in Aichi Prefecture, Japan, an energy consumption model is proposed and calibrated through ordinary least squares regression and multilevel mixed effects linear regression. Specifically, this study focuses on how the ambient temperature affects electricity consumption. Moreover, the interactive effects of ambient temperature and vehicle auxiliary loads are explored. According to the results, the ambient temperature affects the energy efficiency significantly by directly influencing the output energy losses and the interactive effects associated with vehicle auxiliary loads. Ignoring the interactive effects between ambient temperature and vehicle auxiliary loads will exaggerate the energy consumption of the heater during warm conditions and underestimate the energy consumption of the air conditioner during cold conditions. The most economic energy efficiency was achieved in the range of 21.8–25.2 °C. The potential energy savings during proper usage of vehicle auxiliary loads is discussed later based on estimated parameters. As a result, a mean of 9.66% electricity will be saved per kilometre by eradicating unreasonable EV auxiliary loads.

[1]  Michael Pecht,et al.  Battery Management Systems in Electric and Hybrid Vehicles , 2011 .

[2]  Yuanyuan Liu,et al.  Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model , 2013 .

[3]  Tahsin Koroglu,et al.  A comprehensive review on estimation strategies used in hybrid and battery electric vehicles , 2015 .

[4]  Stefano Cordiner,et al.  Trip-based SOC management for a plugin hybrid electric vehicle , 2016 .

[5]  Chao Hu,et al.  Online estimation of lithium-ion battery capacity using sparse Bayesian learning , 2015 .

[6]  Tie-Qiao Tang,et al.  Influences of vehicles' fuel consumption and exhaust emissions on the trip cost without late arrival under car-following model , 2016 .

[7]  He Yin,et al.  Quantitative Efficiency and Temperature Analysis of Battery-Ultracapacitor Hybrid Energy Storage Systems , 2016, IEEE Transactions on Sustainable Energy.

[8]  Thomas H. Bradley,et al.  Estimating the HVAC energy consumption of plug-in electric vehicles , 2014 .

[9]  Takayuki Morikawa,et al.  Modelling the multilevel structure and mixed effects of the factors influencing the energy consumption of electric vehicles , 2016 .

[10]  Toshiyuki Yamamoto,et al.  Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations , 2017 .

[11]  Margaret O'Mahony,et al.  Environmental impacts of varying electric vehicle user behaviours and comparisons to internal combustion engine vehicle usage – An Irish case study , 2016 .

[12]  Anita Graser,et al.  Sensitivity analysis for energy demand estimation of electric vehicles , 2016 .

[13]  Gae-won You,et al.  Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach , 2016 .

[14]  Margaret O'Mahony,et al.  Development of a driving cycle to evaluate the energy economy of electric vehicles in urban areas , 2016 .

[15]  A. Roskilly,et al.  Novel technologies and strategies for clean transport systems , 2015 .

[16]  Yves Dube,et al.  A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures , 2016 .

[17]  Guolin Wang,et al.  New evaluation methodology of regenerative braking contribution to energy efficiency improvement of electric vehicles , 2016 .

[18]  Celil Ozkurt,et al.  Integration of sampling based battery state of health estimation method in electric vehicles , 2016 .

[19]  Stuart C Burgess,et al.  A parametric study of the energy demands of car transportation: a case study of two competing commuter routes in the UK , 2003 .

[20]  Wolfgang Rosenstiel,et al.  HVAC system modeling for range prediction of electric vehicles , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[21]  Hesham Rakha,et al.  Power-based electric vehicle energy consumption model: Model development and validation , 2016 .

[22]  Jianqiu Li,et al.  Thermal Modeling of a LiFePO4/Graphite Battery and Research on the Influence of Battery Temperature Rise on EV Driving Range Estimation , 2013, 2013 IEEE Vehicle Power and Propulsion Conference (VPPC).

[23]  Yu Peng,et al.  Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction , 2013 .

[24]  Matthieu Dubarry,et al.  From driving cycle analysis to understanding battery performance in real-life electric hybrid vehicle operation , 2007 .

[25]  Hai-Jun Huang,et al.  Influences of the driver’s bounded rationality on micro driving behavior, fuel consumption and emissions , 2015 .

[26]  Wei He,et al.  State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures , 2014 .

[27]  J. Jaccard,et al.  Interaction effects in multiple regression , 1992 .

[28]  Hewu Wang,et al.  Energy consumption of electric vehicles based on real-world driving patterns: A case study of Beijing , 2015 .

[29]  Chaoyang Wang,et al.  Lithium-ion battery structure that self-heats at low temperatures , 2016, Nature.

[30]  Guoqing Xu,et al.  An Intelligent Regenerative Braking Strategy for Electric Vehicles , 2011 .

[31]  I D Greenwood,et al.  Estimating the Effects of Traffic Congestion on Fuel Consumption and Vehicle Emissions Based on Acceleration Noise , 2007 .

[32]  Hesham Rakha,et al.  ESTIMATING VEHICLE FUEL CONSUMPTION AND EMISSIONS BASED ON INSTANTANEOUS SPEED AND ACCELERATION LEVELS , 2002 .

[33]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[34]  Peng Hao,et al.  Trajectory-based vehicle energy/emissions estimation for signalized arterials using mobile sensing data , 2015 .

[35]  Takayuki Morikawa,et al.  Impact of road gradient on energy consumption of electric vehicles , 2017 .

[36]  Kai He,et al.  Analysis of downshift’s improvement to energy efficiency of an electric vehicle during regenerative braking , 2016 .

[37]  Xue Wang,et al.  Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error , 2014 .

[38]  Oriol Travesset-Baro,et al.  Transport energy consumption in mountainous roads. A comparative case study for internal combustion engines and electric vehicles in Andorra , 2015 .

[39]  Jianqiu Li,et al.  A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications , 2015 .

[40]  Xinkai Wu,et al.  Electric vehicles’ energy consumption measurement and estimation , 2015 .

[41]  H. Bosma,et al.  Analysis of regenerative braking efficiency — A case study of two electric vehicles operating in the Rotterdam area , 2011, 2011 IEEE Vehicle Power and Propulsion Conference.

[42]  Lin Yang,et al.  Prior-knowledge-independent equalization to improve battery uniformity with energy efficiency and time efficiency for lithium-ion battery , 2016 .